This paper demonstrate, using sales information and SAS Enterprise Miner, how to uncover relative price bands where price can be increased without losing market share or decreased slightly to gain market share.

This paper shows you how to leverage
SAS Asset Performance Analytics and SAS Enterprise Miner to build a model for drilling and well control anomalies, to fingerprint key well control measures of the transient fluid properties, and to
operationalize these analytics on the drilling assets with SAS Event Stream Processing.

This paper first summarizes the problems that were specified and data that were supplied by the Challenge sponsors at Cloudera. Then it outlines the techniques and technologies used to complete
the Challenge, followed by sections that describe in greater detail the approaches used for data preprocessing and for completing the Challenge deliverables.

This paper first explains the concepts of association discovery, sequence discovery, multiple centrality measures and clustering coefficient measure, and item clusters. Then it shows how
the Link Analysis node incorporates these concepts in analyzing transactional data. The paper also shows how you can adapt non-transactional data to the link analysis framework. Finally,
examples illustrate how to use the Link Analysis node to analyze Netflix data and Fisher’s Iris data.

SAS Global Forum Papers

This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as
rotational forest and fuzzy clustering. The examples section presents a quick setup that enables you to take fullest advantage of the ensemble capabilities of SAS Enterprise Miner by using existing
nodes, Start Groups and End Groups nodes, and custom coding.

2012 Papers

The Credit Scoring add-on in SAS Enterprise Miner is widely used to build binary target (good, bad) scorecards for probability of default.
The process involves grouping variables using weight of evidence, and then performing logistic regression to produce predicted probabilities. This paper will demonstrate how to use the same tools to build
binned variable scorecards for Loss Given Default, explaining the theoretical principles behind the method and use actual data to demonstrate how it was done.

This paper compares the performance of the HPGENSELECT procedure with results cited for the RevoScaleR package by using data that are similar to the insurer's data. The paper also demonstrates the
scalability of the HPGENSELECT procedure by using two sizes of data sets and three different computing environments.

2014 Papers

This paper takes a quick look at how to organize and analyze textual data for extracting insightful customer intelligence from a large collection of documents and for using such information
to improve business operations and performance.

SAS Text Miner 12.1 and SAS Content Categorization Studio 12.1 is used to develop a rule-based categorization model. This model is then used to automatically score a paper abstract to identify the
most relevant and appropriate conference sections to submit to for a better chance of acceptance.

This paper demonstrates a new and powerful feature in SAS Text Miner 12.1 which helps in explaining the SVDs or the text cluster components. Discussed also are two important methods useful to interpret them.

This paper demonstrates how to use SAS Text Miner procedures to process sparse data sets and gen-erate output data sets that are easy to store and can be readily processed by traditional SAS modeling procedures.